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Carbonate Brazilian pre-salt fields have a large number of faults detected by seismic and well data. Nevertheless, because of limitations in seismic resolution, all existent faults cannot be identified. That is one of the main challenges for understanding related heterogeneities (vugs, karst) and the flow behavior. This paper deals with a fault analysis and modeling using an original approach and fault data of three pre-salt reservoirs.
One possible approach for characterizing and modeling the fault network (
The results presented on this article lead us to discuss the importance of how to choose the samples for modeling sub-seismic faults based on the ensemble of seismic faults available. This article answers the question about which available seismic faults we should use for estimating fractal dimension, should we use all available seismic faults near of the reservoir area or use only the faults inside the reservoir contour. After this short discussion on the fractal dimension choice from a spatial distribution point of view, the impact of this choice on flow was illustrated. The sub-seismic fault models were modeled using different fractal dimension. Subsequently, an upscaling step using analytical upscaling (
Characterizing sub-seismic faults has a major impact on the overall flow behavior of the field. The chosen methodology has been applied only on synthetic cases but never published using real data. This work will interest a practicing engineer. The fault network of these neighbor reservoirs allows us to illustrate the importance on the choice of fractal dimension for characterizing the fault network and its impact on the subseismic models and fluid displacement, consequently on production.
An integrated optimization workflow was developed to characterize seismic and sub-seismic fault networks from history-matching. A fractal model of fault networks is optimized via the gradual deformation of stochastic realizations of fault density maps, fault spatial and length distributions. In order to facilitate the history-matching, connectivity analysis tools were developed for characterizing wells-reservoir and well-to-well connectivity. Indeed these connectivity properties usually depend on the fault network realization and may be strongly correlated with the reservoir flow dynamics. Connectivity analyses were performed on a fractured reservoir model involving a five-spot well configuration with four injectors and one producer. The connectivity was estimated from shortest path algorithms applied on a graph representation of the reservoir model. Several reservoir simulations were performed for different fault network realizations to seek correlations between injector-producer connectivity and water breakthrough time. The impact of the fracture properties uncertainties on the wells-reservoir connectivity was estimated via the cumulated connected volume computed for each well. This connectivity measure provides a mean to characterize and classify fault network realizations. Correlations were found between the water breakthrough time and the injector-producer connectivity, thus allowing one to identify the most probable fault network realizations to match the observed water breakthrough time. Finally, for a given fault network realization, it is shown how the oil recovery can be optimized by correlating injectors rates with the injector-producer connectivity. A gain of 3.106 m3 in produced oil was obtained, while retarding the water breakthrough time by 16 years, compared with a case where all injectors have the same rate. The proposed methodology and tools facilitate the history-matching of fractured reservoir, providing consistent reservoir models that can be used for production forecast and optimization.